PONE-D-25-39265MPLNet:Mamba Prompt Learning Network for Semantic Segmentation of Remote Sensing Images of Traditional VillagesPLOS ONE

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“We thank the National Natural Science Foundation of China for supporting thisresearch through the projects "Gene Identification and Map Construction of TraditionaRural Settlement Landscapes in the Ganjiang River Basin”(Serial No. 51968026) and"Research on the Visual Perception, Quantitative Characterization, and VisualEvaluation of Traditional Village Landscape Resources in Ganjiang River Basin”(SerialNo.52268012). We also acknowledge the support of Jiangxi Rural Culture DevelopmentResearch Center. We appreciate the technical assistance provided by the GIS andRemote Sensing Laboratory at Jiangxi Agricultural University. Special thanks to allmembers of the research team for their valuable discussions and contributions to theproject.”

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Reviewers' comments:

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Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

Reviewer #2: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This paper addresses the existing issues in semantic segmentation of remote sensing images (RSI) of traditional villages, constructs the Traditional Villages Remote Sensing Dataset (TV-RSI) — a large-scale and highly diverse remote sensing dataset for traditional villages, designs the Mamba Fusion Module (MFM) which performs intra-modal contextual modeling on modal features via the Mamba mechanism, and proposes the Mamba Prompt Learning Network (MPLNet). Experiments have been conducted on the TV-RSI dataset and two public datasets (Potsdam and Wassingen) to verify the effectiveness of the proposed method. However, improvements are required in the following aspects:

1.There are irregular expressions and grammatical errors in the manuscript.

2.The definitions of symbols in formulas are incomplete. For instance, "C3S" in Formula (1) is not clearly explained in the text; although "r_i", "P_i", and "Q_i" in Formulas (5) and (6) are defined, key attributes such as their dimensions and data types are not specified.

3.Regarding the workflow of the MFM, only "intra-modal contextual modeling is performed via the Mamba mechanism" is mentioned, while the specific way in which the Mamba mechanism achieves multi-directional contextual information acquisition is not elaborated.

4.For comparative experiments, the proposed model is only compared with relatively early or domain-specific models such as FCN-8s and ACNet, and mainstream remote sensing image semantic segmentation models in recent years are not included in the comparison.

5.In the ablation experiments, only the improvement effects of the MFM and prompt learning on metrics are presented, while the specific performance when these modules are disabled is not analyzed.

6.The unique value of MPLNet in practical scenarios of traditional village protection is not clearly illustrated. For example, its advantages over existing technologies in specific tasks such as accurate identification of cultural heritage buildings and division of village ecological boundaries are not specified, making it difficult to demonstrate the practical significance of the innovation.

Reviewer #2: The manuscript proposes a novel network architecture, MPLNet, introducing a Mamba Fusion Module (MFM) and prompt learning strategy for semantic segmentation of traditional village remote sensing images. The paper also constructs a new dataset (TV-RSI), which appears valuable for this niche but important domain. The overall contribution is relevant to PLOS ONE, considering its emphasis on methodological novelty and real-world application. However, while the manuscript contains promising ideas, several issues should be addressed before it can be considered for publication.

1. In Lines 172–179, the statement “the enriched information from the frozen part is injected into the RGB features…” needs further elaboration. How is this "enrichment" computed and aligned spatially/semantically?

2. The overall architecture description in Fig. 4 is vague. The explanation about the “frozen part” and “trainable part” lacks clarity on what exactly is frozen and why. Please clearly specify which layers are frozen, and how this design affects the gradient flow and optimization.

3. The formulas in Lines 193–196 are insufficiently explained. For instance:

What does SS2D actually do? This “selective scanning” operation needs to be precisely defined or cited.

What is the rationale for using SiLU activation, and how does it perform compared to other activations in this context?

4. The formulas (5) and (6) are ambiguous:

What is M(ri) in Equation (5)? Is ri a feature map from the frozen network? If yes, how are they aligned with Fi?

Equation (6) includes Pi × Qi + Qi which seems redundant unless clarified. Please provide intuitive or geometric interpretation of this operation.

5. The authors should analyze parameter count and FLOPs to support claims of “lightweight” architecture (repeated throughout the manuscript).

6. The dataset is said to have 77,850 images (Line 118). However, this number seems high for a manually annotated semantic segmentation dataset. Please clarify:

Are these full-resolution images or image patches?

What is the annotation protocol? Was any manual QA performed?

7. There are frequent grammar issues and awkward phrases, such as:

Line 14: “enabling more precise spatial modeling…” → consider rephrasing for clarity.

Line 23: “siting and landscape pattern… show a deep understanding…” → this anthropomorphizes patterns; rephrase more precisely.

Please consider a thorough language editing pass to improve readability. Several sentences are overly long and dense, especially in the Introduction and Dataset sections.

8. In literature review, several highly cited works concerning the CNN and transformers for remote sensing image processing are suggested to be discussed, such as SAPNet (TGRS 2023), GPINet (TGRS 2023), FDNet (TGRS 2025), DDFNet (information fusion), and so forth.

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Reviewer #1: No

Reviewer #2: No

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MPLNet:Mamba Prompt Learning Network for Semantic Segmentation of Remote Sensing Images of Traditional Villages

PONE-D-25-39265R1

Dear Dr. Liu,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Kind regards,

Mahmoud Emam, Ph.D.

Academic Editor

PLOS One

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #2: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #2: Yes

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: Yes

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4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #2: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #2: Yes

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #2: The authors have addressed all the concerns.

I have no more concerns.

It is ready for acceptance for publication.

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7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #2: No

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